OpenRaiser/Intern-Atlas huggingface dataset:
Measuring Fatigue of Soldiers in Wireless Body Area Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

Wireless Body Area Sensor Networks (WBASNs) consist of on-body or in-body sensors placed on human body for health monitoring. Energy conservation of these sensors, while guaranteeing a required level of performance, is a challenging task. Energy efficient routing schemes are designed for the longevity of network lifetime. In this paper, we propose a routing protocol for measuring fatigue of a soldier. Three sensors are attached to soldier's body that monitor specific parameters. Our proposed protocol is an event driven protocol and takes three scenarios for measuring the fatigue of a soldier. We evaluate our proposed work in terms of network lifetime, throughput, remaining energy of sensors and fatigue of a soldier.
AM-DisCNT: Angular Multi-hop DIStance based Circular Network Transmission Protocol for WSNs
Venue: arXiv cs.NI 2013
Year: 2013

The nodes in wireless sensor networks (WSNs) contain limited energy resources, which are needed to transmit data to base station (BS). Routing protocols are designed to reduce the energy consumption. Clustering algorithms are best in this aspect. Such clustering algorithms increase the stability and lifetime of the network. However, every routing protocol is not suitable for heterogeneous environments. AM-DisCNT is proposed and evaluated as a new energy efficient protocol for wireless sensor networks. AM-DisCNT uses circular deployment for even consumption of energy in entire wireless sensor network. Cluster-head selection is on the basis of energy. Highest energy node becomes CH for that round. Energy is again compared in the next round to check the highest energy node of that round. The simulation results show that AM-DisCNT performs better than the existing heterogeneous protocols on the basis of network lifetime, throughput and stability of the system.
Data Warehouse Success and Strategic Oriented Business Intelligence: A Theoretical Framework
Venue: arXiv cs.DB 2013
Year: 2013

With the proliferation of the data warehouses as supportive decision making tools, organizations are increasingly looking forward for a complete data warehouse success model that would manage the enormous amounts of growing data. It is therefore important to measure the success of these massive projects. While general IS success models have received great deals of attention, few research has been conducted to assess the success of data warehouses for strategic business intelligence purposes. The framework developed in this study consists of the following nine measures: Vendors and Consultants, Management Actions, System Quality, Information Quality, Data Warehouse Usage, Perceived utility, Individual Decision Making Impact, Organizational Decision Making Impact, and Corporate Strategic Goals Attainment.
Towards an Efficient Discovery of the Topological Representative Subgraphs
Venue: arXiv cs.DB 2013
Year: 2013

With the emergence of graph databases, the task of frequent subgraph discovery has been extensively addressed. Although the proposed approaches in the literature have made this task feasible, the number of discovered frequent subgraphs is still very high to be efficiently used in any further exploration. Feature selection for graph data is a way to reduce the high number of frequent subgraphs based on exact or approximate structural similarity. However, current structural similarity strategies are not efficient enough in many real-world applications, besides, the combinatorial nature of graphs makes it computationally very costly. In order to select a smaller yet structurally irredundant set of subgraphs, we propose a novel approach that mines the top-k topological representative subgraphs among the frequent ones. Our approach allows detecting hidden structural similarities that existing approaches are unable to detect such as the density or the diameter of the subgraph. In addition, it can be easily extended using any user defined structural or topological attributes depending on the sought properties. Empirical studies on real and synthetic graph datasets show that our approach is fast and scalable.
RepFlow: Minimizing Flow Completion Times with Replicated Flows in Data Centers
Venue: arXiv cs.NI 2013
Year: 2013

Short TCP flows that are critical for many interactive applications in data centers are plagued by large flows and head-of-line blocking in switches. Hash-based load balancing schemes such as ECMP aggravate the matter and result in long-tailed flow completion times (FCT). Previous work on reducing FCT usually requires custom switch hardware and/or protocol changes. We propose RepFlow, a simple yet practically effective approach that replicates each short flow to reduce the completion times, without any change to switches or host kernels. With ECMP the original and replicated flows traverse distinct paths with different congestion levels, thereby reducing the probability of having long queueing delay. We develop a simple analytical model to demonstrate the potential improvement of RepFlow. Extensive NS-3 simulations and Mininet implementation show that RepFlow provides 50%--70% speedup in both mean and 99-th percentile FCT for all loads, and offers near-optimal FCT when used with DCTCP.
An Approach Finding Frequent Items In Text Or Transactional Data Base By Using BST To Improve The Efficiency Of Apriori Algorithm
Venue: arXiv cs.DB 2013
Year: 2013

Data mining techniques have been widely used in various applications. Binary search tree based frequent items is an effective method for automatically recognize the most frequent items, least frequent items and average frequent items. This paper presents a new approach in order to find out frequent items. The word frequent item refers to how many times the item appeared in the given input. This approach is used to find out item sets in any order using familiar approach binary search tree. The method adapted here is in order to find out frequent items by comparing and incrementing the counter variable in existing transactional data base or text data. We are also representing different approaches in frequent item sets and also propose an algorithmic approach for the problem solving
Multi-Objective Beamforming for Secure Communication in Systems with Wireless Information and Power Transfer
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we study power allocation for secure communication in a multiuser multiple-input single-output (MISO) downlink system with simultaneous wireless information and power transfer. The receivers are able to harvest energy from the radio frequency when they are idle. We propose a multi-objective optimization problem for power allocation algorithm design which incorporates two conflicting system objectives: total transmit power minimization and energy harvesting efficiency maximization. The proposed problem formulation takes into account a quality of service (QoS) requirement for the system secrecy capacity. Our designs advocate the dual use of artificial noise in providing secure communication and facilitating efficient energy harvesting. The multi-objective optimization problem is non-convex and is solved by a semidefinite programming (SDP) relaxation approach which results in an approximate of solution. A sufficient condition for the global optimal solution is revealed and the accuracy of the approximation is examined. To strike a balance between computational complexity and system performance, we propose two suboptimal power allocation schemes. Numerical results not only demonstrate the excellent performance of the proposed suboptimal schemes compared to baseline schemes, but also unveil an interesting trade-off between energy harvesting efficiency and total transmit power.
Computer Aided Investigation: Visualization and Analysis of data from Mobile communication devices using Formal Concept Analysis
Venue: arXiv cs.CY 2013
Year: 2013

In this world of terrorism, it is very important to know the network of individual suspects. It is also important to analyze the attributes of members of a network and the relationships that exist between them either directly or indirectly. This will make it easy for concepts to be built in aiding criminal investigations. However traditional approaches cannot be used to visualize and analyze data collected on individuals. With this current day where information systems play critical role in everyday life of every individual, it is easier to depend on digital information in fighting crime.Effective computer tools and intelligent systems that are automated to analyze and interpret criminal data in real time effectively and efficiently are needed in fighting crime. These current computer systems should have the capability of providing intelligence from raw data and creating a visual graph which will make it easy for new concepts to be built and generated from crime data in order to solve understand and analyze crime patterns easily. This paper proposes a new method of computer aided investigation by visualizing and analyzing data of mobile communication devices using Formal Concept Analysis, or Galois Lattices, a data analysis technique grounded on Lattice Theory and Propositional Calculus. This method considered the set of common and distinct attributes of data in such a way that categorizations are done based on related data with respect to time and events. This will help in building a more defined and conceptual systems for analysis of crime data that can easily be visualized and intelligently analyzed by computer systems.
Multi-dimensional Parametric Mincuts for Constrained MAP Inference
Venue: arXiv cs.LG 2013
Year: 2013

In this paper, we propose novel algorithms for inferring the Maximum a Posteriori (MAP) solution of discrete pairwise random field models under multiple constraints. We show how this constrained discrete optimization problem can be formulated as a multi-dimensional parametric mincut problem via its Lagrangian dual, and prove that our algorithm isolates all constraint instances for which the problem can be solved exactly. These multiple solutions enable us to even deal with `soft constraints' (higher order penalty functions). Moreover, we propose two practical variants of our algorithm to solve problems with hard constraints. We also show how our method can be applied to solve various constrained discrete optimization problems such as submodular minimization and shortest path computation. Experimental evaluation using the foreground-background image segmentation problem with statistic constraints reveals that our method is faster and its results are closer to the ground truth labellings compared with the popular continuous relaxation based methods.
Finite-State Markov Modeling of Tunnel Channels in Communication-based Train Control (CBTC) Systems
Venue: arXiv cs.DM 2013
Year: 2013

Communication-based train control (CBTC) is gradually adopted in urban rail transit systems, as it can significantly enhance railway network efficiency, safety and capacity. Since CBTC systems are mostly deployed in underground tunnels and trains move in high speed, building a train-ground wireless communication system for CBTC is a challenging task. Modeling the tunnel channels is very important to design and evaluate the performance of CBTC systems. Most of existing works on channel modeling do not consider the unique characteristics in CBTC systems, such as high mobility speed, deterministic moving direction, and accurate train location information. In this paper, we develop a finite state Markov channel (FSMC) model for tunnel channels in CBTC systems. The proposed FSMC model is based on real field CBTC channel measurements obtained from a business operating subway line. Unlike most existing channel models, which are not related to specific locations, the proposed FSMC channel model takes train locations into account to have a more accurate channel model. The distance between the transmitter and the receiver is divided into intervals, and an FSMC model is applied in each interval. The accuracy of the proposed FSMC model is illustrated by the simulation results generated from the model and the real field measurement results.
Algorithms for the Majority Rule (+) Consensus Tree and the Frequency Difference Consensus Tree
Venue: arXiv cs.DS 2013
Year: 2013

This paper presents two new deterministic algorithms for constructing consensus trees. Given an input of k phylogenetic trees with identical leaf label sets and n leaves each, the first algorithm constructs the majority rule (+) consensus tree in O(kn) time, which is optimal since the input size is Omega(kn), and the second one constructs the frequency difference consensus tree in min(O(kn^2), O(kn (k+log^2 n))) time.
Characterizing Compatibility and Agreement of Unrooted Trees via Cuts in Graphs
Venue: arXiv cs.DM 2013
Year: 2013

Deciding whether there is a single tree -a supertree- that summarizes the evolutionary information in a collection of unrooted trees is a fundamental problem in phylogenetics. We consider two versions of this question: agreement and compatibility. In the first, the supertree is required to reflect precisely the relationships among the species exhibited by the input trees. In the second, the supertree can be more refined than the input trees. Tree compatibility can be characterized in terms of the existence of a specific kind of triangulation in a structure known as the display graph. Alternatively, it can be characterized as a chordal graph sandwich problem in a structure known as the edge label intersection graph. Here, we show that the latter characterization yields a natural characterization of compatibility in terms of minimal cuts in the display graph, which is closely related to compatibility of splits. We then derive a characterization for agreement.
Hybrid Affinity Propagation
Venue: arXiv cs.CV 2013
Year: 2013

In this paper, we address a problem of managing tagged images with hybrid summarization. We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images. We propose a novel approach, called homogeneous and heterogeneous message propagation ($\text{H}^\text{2}\text{MP}$). Similar to the affinity propagation (AP) approach, $\text{H}^\text{2}\text{MP}$ reduce the conventional \emph{vector} message propagation to \emph{scalar} message propagation to make the algorithm more efficient. Beyond AP that can only handle homogeneous data, $\text{H}^\text{2}\text{MP}$ generalizes it to exploit extra heterogeneous relations and the generalization is non-trivial as the reduction to scalar messages from vector messages is more challenging. The main advantages of our approach lie in 1) that $\text{H}^\text{2}\text{MP}$ exploits visual similarity and in addition the useful information from the associated tags, including the associations relation between images and tags and the relations within tags, and 2) that the summary is both visually and semantically satisfactory. In addition, our approach can also present a textual summary to a tagged image collection, which can be used to automatically generate a textual description. The experimental results demonstrate the effectiveness and efficiency of the roposed approach.
Complexity Analysis in Cyclic Tag System Emulated by Rule 110
Venue: arXiv cs.OH 2013
Year: 2013

It is known that elementary cellular automaton rule 110 is capable of supporting universal computation by emulating cyclic tag system. Since the whole information necessary to perform computation is stored in the configuration, it is reasonable to investigate the complexity of configuration for the analysis of computing process. In this research we employed Lempel-Ziv complexity as a measure of complexity and calculated it during the evolution of emulating cyclic tag system by rule 110. As a result, we observed the stepwise decline of complexity during the evolution. That is caused by the transformation from table data to moving data and the elimination of table data by a rejector.
Extracting Information-rich Part of Texts using Text Denoising
Venue: arXiv cs.IR 2013
Year: 2013

The aim of this paper is to report on a novel text reduction technique, called Text Denoising, that highlights information-rich content when processing a large volume of text data, especially from the biomedical domain. The core feature of the technique, the text readability index, embodies the hypothesis that complex text is more information-rich than the rest. When applied on tasks like biomedical relation bearing text extraction, keyphrase indexing and extracting sentences describing protein interactions, it is evident that the reduced set of text produced by text denoising is more information-rich than the rest.
Visualization and Analysis of Geographical Crime Patterns Using Formal Concept Analysis
Venue: arXiv cs.CY 2013
Year: 2013

There are challenges faced in today's world in terms of crime analysis when it comes to graphical visualization of crime patterns. Geographical representation of crime scenes and crime types become very important in gathering intelligence about crimes. This provides a very dynamic and easy way of monitoring criminal activities and analyzing them as well as producing effective countermeasures and preventive measures in solving them. But we need effective computer tools and intelligent systems that are automated to analyze and interpret criminal data in real time effectively and efficiently. These current computer systems should have the capability of providing intelligence from raw data and creating a visual graph which will make it easy for new concepts to be built and generated from crime data in order to solve, understand and analyze crime patterns easily. This paper proposes a new method of visualizing and analyzing crime patterns based on geographical crime data by using Formal Concept Analysis, or Galois Lattices, a data analysis technique grounded on Lattice Theory and Propositional Calculus. This method considered the set of common and distinct attributes of crimes in such a way that categorization are done based on related crime types. This will help in building a more defined and conceptual systems for analysis of geographical crime data that can easily be visualized and intelligently analyzed by computer systems.
A Size-Sensitive Discrepancy Bound for Set Systems of Bounded Primal Shatter Dimension
Venue: arXiv cs.CG 2013
Year: 2013

Let $(X,§)$ be a set system on an $n$-point set $X$. The \emph{discrepancy} of $§$ is defined as the minimum of the largest deviation from an even split, over all subsets of $S \in §$ and two-colorings $χ$ on $X$. We consider the scenario where, for any subset $X' \subseteq X$ of size $m \le n$ and for any parameter $1 \le k \le m$, the number of restrictions of the sets of $§$ to $X'$ of size at most $k$ is only $O(m^{d_1} k^{d-d_1})$, for fixed integers $d > 0$ and $1 \le d_1 \le d$ (this generalizes the standard notion of \emph{bounded primal shatter dimension} when $d_1 = d$). In this case we show that there exists a coloring $χ$ with discrepancy bound $O^{*}(|S|^{1/2 - d_1/(2d)} n^{(d_1 - 1)/(2d)})$, for each $S \in §$, where $O^{*}(\cdot)$ hides a polylogarithmic factor in $n$. This bound is tight up to a polylogarithmic factor \cite{Mat-95, Mat-99} and the corresponding coloring $χ$ can be computed in expected polynomial time using the very recent machinery of Lovett and Meka for constructive discrepancy minimization \cite{LM-12}. Our bound improves and generalizes the bounds obtained from the machinery of Har-Peled and Sharir \cite{HS-11} (and the follow-up work in \cite{SZ-12}) for points and halfspaces in $d$-space for $d \ge 3$.
Improved Median Polish Kriging for Simulation Metamodeling
Venue: arXiv cs.OH 2013
Year: 2013

In simulation, Median Polish Kriging is a technique used to predict unobserved data points in two-dimensional space. The linear behavior of the traditional Median Polish Kriging in the estimation of the mean function in a high grid makes the interpolation of O(1) which has a low order in the prediction and that leads to a high prediction error. Therefore, an improvement in the estimation of the mean function has been introduced using Biharmonic spline interpolation and the new technique has been called Improved Median Polish Kriging (IMPK). The IMPK has been applied to the standard coal-ash data in two-dimension. The novel method gave much better results according to the cross validation results that were obtained when compared with the traditional Median Polish Kriging.
Non-homogeneous Two-Rack Model for Distributed Storage Systems
Venue: arXiv cs.IT 2013
Year: 2013

In the traditional two-rack distributed storage system (DSS) model, due to the assumption that the storage capacity of each node is the same, the minimum bandwidth regenerating (MBR) point becomes infeasible. In this paper, we design a new non-homogeneous two-rack model by proposing a generalization of the threshold function used to compute the tradeoff curve. We prove that by having the nodes in the rack with higher regenerating bandwidth stores more information, all the points on the tradeoff curve, including the MBR point, become feasible. Finally, we show how the non-homogeneous two-rack model outperforms the traditional model in the tradeoff curve between the storage per node and the repair bandwidth.
Performing Implicit Induction Reasoning with Certifying Proof Environments
Venue: arXiv cs.LO 2013
Year: 2013

Largely adopted by proof assistants, the conventional induction methods based on explicit induction schemas are non-reductive and local, at schema level. On the other hand, the implicit induction methods used by automated theorem provers allow for lazy and mutual induction reasoning. In this paper, we present a new tactic for the Coq proof assistant able to perform automatically implicit induction reasoning. By using an automatic black-box approach, conjectures intended to be manually proved by the certifying proof environment that integrates Coq are proved instead by the Spike implicit induction theorem prover. The resulting proofs are translated afterwards into certified Coq scripts.
Tracking Extrema in Dynamic Environment using Multi-Swarm Cellular PSO with Local Search
Venue: arXiv cs.AI 2013
Year: 2013

Many real-world phenomena can be modelled as dynamic optimization problems. In such cases, the environment problem changes dynamically and therefore, conventional methods are not capable of dealing with such problems. In this paper, a novel multi-swarm cellular particle swarm optimization algorithm is proposed by clustering and local search. In the proposed algorithm, the search space is partitioned into cells, while the particles identify changes in the search space and form clusters to create sub-swarms. Then a local search is applied to improve the solutions in the each cell. Simulation results for static standard benchmarks and dynamic environments show superiority of the proposed method over other alternative approaches.
The Likelihood Encoder for Source Coding
Venue: arXiv cs.IT 2013
Year: 2013

The likelihood encoder with a random codebook is demonstrated as an effective tool for source coding. Coupled with a soft covering lemma (associated with channel resolvability), likelihood encoders yield simple achievability proofs for known results, such as rate-distortion theory. They also produce a tractable analysis for secure rate-distortion theory and strong coordination.